Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction

Abstract

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.

A Novel Method of Fault Detection and Identification in a Tightly Coupled, INS/GNSS-Integrated System

“…where z k is the innovation of the Kalman filter, which is the same as the one defined in Equation (12). x f rame is the vector of position errors and receiver clock biases, whose dimensions are equal to three plus the number of observed constellations.…”

“…Extended receiver autonomous integrity monitoring introduced the error model of the nonlinear filter into the monitoring process [ 11 ]. Two independent detectors exist for GNSS faults and filter faults: an exclusion function can be utilized for the identification and removal of the faulty measurements, and elimination of the filter fault effect is carried out through filter recovery [ 12 ]. The former method involves an adaptive filter or a robust filter.…”

Kalman Filter-Based RAIM for Reliable Aircraft Positioning with GPS and NavIC Constellations

“…Integrity monitoring for integrated GNSS-inertial navigation system (INS) was recently studied in [ 34 , 35 , 36 , 37 , 38 ] in both range and position domains for KF. In [ 34 ] an integrity monitor is presented in the range domain to deal with spoofing attacks during an aircraft approach to a runway.…”

“…Solution separation RAIM for tightly coupled GNSS-INS is described in [ 37 ] for precise point positioning. A fault detection and exclusion scheme for tightly coupled GNSS-INS system is designed in [ 38 ]; it can detect multiple GNSS, and simultaneous GNSS and INS faults.…”

“…While integrity monitoring with WLS is extensively studied and well-developed [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ], relatively few studies are available on its KF counterpart [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. To this end, a novel KF-based integrity algorithm is designed in this paper for reliable absolute positioning with GNSS pseudorange (carrier smoothed) and pseudorange rate measurements.…”